This invention relates generally to diagnosing mechanical problems in rotating machinery such as gas or steam turbines and more particularly concerns a computer-aided diagnostic system and method which combine AI-based interpretive reasoning with rotordynamics-based modeling and numerical optimization.
Rotating machinery such as power generating equipment almost inevitably develops some mechanical problems over time. Almost all of these mechanical problems, such as unbalance or misalignment, produce a synchronous vibration signal. Vibrations in rotating machinery can ultimately lead to fatigue and even failure of components; thus, the smoother a piece of equipment runs, the longer and more trouble-free its life will be. Therefore, it is beneficial to take corrective measures soon after the appearance of such problems. Such maintenance is particularly important in the power generation industry because over fifty percent of the major power equipment currently in operation has been in service for 25 years or longer. Continued availability of power from these machines at a reasonable cost is one of the most important economic factors in power plant operation.
The most common corrective measure is to add counteracting balance weights which correct a mass unbalance. However, experience with gas and steam turbines has shown that mass unbalance is the problem only about 30 percent of the time. For the rest of the time, the addition of balance weights will produce only a temporary vibration reduction, or none at all. Furthermore, conventional balancing techniques can be very time consuming and require the unit to be taken off-line during the corrective procedure. Such shutdowns are very costly to power generation plant operators. Thus, there is much interest in quickly and accurately diagnosing mechanical problems in rotating systems such as steam-turbine generator units to reduce forced shutdowns and maintenance costs in power plants.
Most vibration diagnostic work is done by experienced troubleshooting engineers based on empirical knowledge. However, expert system technology which automates the empirical knowledge is finding applications in power plant troubleshooting and maintenance practices. Current diagnostic expert systems provide "probabilistic" and "qualitative" diagnoses with only a limited capability of differentiating between various mechanical problems such as mass unbalance, misalignment, rubbing and so forth. These expert systems have knowledgebases consisting of specific rules which capture currently available knowledge. The diagnostic rules are developed from both analytical modeling and past experience. These rules are thus limited to diagnosing problems which have been identified or modeled in the past. Thus, there is a need for a diagnostic system which is not limited to qualitative solutions based on probable causes. More specifically, there is a need for a system which not only determines the specific flaw or flaws causing the mechanical vibrations, but also determines the location and severity of the flaws.